There are a lots of domain to publish a paper in computer science background where I choose Recommender System related to Data Mining domain. Because of the overflow of information that is available over the internet makes information seeking more a difficult task. And here I can apply better algorithm to make easy users recommendation.
Recommendation are systems that help select out similar things whenever you select something online. Basically, Recommended learning resources are computed based on the current learner’s recent navigation history, as well as exploiting similarities and dissimilarities among learners’ preferences and educational content. There are numerous good quality courses on recommender on movie’s and e-commerce on different topics. I faced a lot of problems when I had to choose a course for me to get introduced to Machine Learning. There were a lot of courses on that domain but due to my lack of knowledge about the domain, I found it difficult to find the appropriate course for me. Then, I thought about recommender System and want to apply on my research. The research in the area of recommendation systems has been going on for several decades now, but the interest still remains high because of the abundance of practical applications and the problem rich domain. Imagine, a user interacts a really large catalog of data daily.
Now, this item could be songs, movies at Netflix, that could be news items from Google. What really matters when billion million data comes? And here comes Recommender system. For example, Netflix will suggest movies list you might want to watch or Pandora will suggest different songs that you might want to listen, Amazon will suggest what kind of other products you might want to buy, Facebook will even suggest some of the other friends that you might want to add. As we see there is a great impact on e-commerce site by using recommender system. Each of this system operates using the same basic kind of algorithm and that’s what I am going to write. Now, this algorithm is a surprisingly big business.
Recommendation systems have recently become important components of numerous computer applications in the e-commerce space. In particular, such systems enable a receipt of feedback from company’s customers. Based on their customer’s feedback, the companies can better serve their customers by providing recommendations and suggestion thereto. Netflix a few years ago in 2009 offered $1 million prizes if you could improve their recommendation system by just 10%. So, this field impact on business hugely. These things motivate me to work with recommendation system.
To make our everyday life easier and to make recommender system more friendly and for our recreation, I will implement a better algorithm, so that we can utilize it in many from like business, social media, entertaining etc. Besides, my personal interest in data mining also motivates to work in the research field. There are a lot of research on this topic. But some of them are ambiguous.
And my intention is to work this topic, implement a better algorithm, remove ambiguity and find a distinctive result that will have a greater effect on a recommendation system.